code: def __init__(self, layer_dims: List[int], dropout: float):
      layers = []
      for i, (in_size, out_size) in enumerate(
          zip(layer_dims, layer_dims[1:])
      ):
          if i > 0:
              layers.append(nn.LeakyReLU())
              if dropout > 0:
                  layers.append(nn.Dropout(dropout))
          layers.append(nn.Linear(in_size, out_size))
      super().__init__(*layers)
  
file_path: gluonts/src/gluonts/nursery/tsbench/src/tsbench/surrogate/torch/deepset.py
imports: from typing import List
  import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new _MLP object from inherited base classes: nn with example: obj = _MLP(layer_dims, dropout)